Program USP
The Unique Selling Proposition (USP) of our AI and Robotics program lies in its interdisciplinary approach, combining cognitive technologies, AI/ML, computer vision, and robotics. This holistic curriculum prepares students to tackle complex challenges by providing them with a comprehensive understanding of the field. Our program stands out by emphasizing practical hands-on experience, encouraging innovation, and promoting ethical considerations in the development of intelligent systems. Additionally, our strong industry partnerships and opportunities for research collaboration ensure that students gain real-world exposure and stay at the forefront of technological advancements.
Few important courses under the program:
- Natural Language Processing: Focuses on techniques for language understanding, sentiment analysis, machine translation, and text generation. Students learn to develop AI systems that can comprehend and generate human language effectively.
- Knowledge Representation and Reasoning: Covers representing and reasoning with knowledge in intelligent systems. Students learn techniques such as semantic networks, frames, and ontologies to enable reasoning and decision-making.
- Cognitive Robotics: Integrates cognitive science principles with robotics, enabling robots to perceive, reason, and act in human-like ways. Students explore topics like sensor integration, cognitive architectures, and human-robot interaction for building intelligent robotic systems.
- Neural Networks and Deep Learning: Emphasizes the fundamentals of neural networks and deep learning algorithms. Students gain expertise in feedforward and recurrent neural networks, convolutional neural networks (CNNs), and generative models for cognitive tasks.
- Cognitive Science: Introduces principles and theories of cognitive science, including perception, memory, and problem-solving. Students gain an understanding of human cognition to inform the development of intelligent systems.
- Human-Computer Interaction: Focuses on designing user-centered interfaces and interactions. Students learn usability testing, user experience design, and cognitive ergonomics to create intuitive and engaging intelligent systems.
- Ethical Considerations in AI: Explores ethical implications and societal impact of AI technologies. Students discuss topics like fairness, transparency, privacy, and accountability to develop AI systems with responsible and ethical practices.
- Machine Learning for Robotics: Applies machine learning techniques to robotics. Students learn reinforcement learning, computer vision algorithms, and motion planning to enable robots to learn and adapt in real-world scenarios.
- Robotic Perception: Focuses on sensor integration and perception algorithms for robotic systems. Students learn to process data from various sensors, enabling robots to perceive and understand their environment cognitively.
- Cognitive Modeling: Introduces cognitive modeling techniques to simulate human thought processes. Students develop computational models based on cognitive theories, enabling intelligent systems to mimic human cognitive abilities.
